Machines such as, for example, track-type tractors, dozers, motor graders, wheel loaders, and the like, are used to perform a variety of tasks. For example, these machines may be used to move material and/or alter work surfaces at a worksite. The machines may be manned machines, but may also be autonomous or semi-autonomous vehicles that perform these tasks in response to commands remotely or locally generated as part of a work plan for the machines. The machines may receive instructions in accordance with the work plan to perform operations, including digging, loosening, carrying, and any other manipulation of materials at the worksite.
It may be desirable to ensure that the machines perform these operations such that the materials are moved in an efficient manner. More particularly, in repetitive operations, it may be especially desirable to ensure that the locations at which the machines begin to alter the work surface and/or the profiles along which the machines alter the work surface are chosen such that the machines function efficiently. Some conventional systems plan cut locations based on predetermined cut volume estimations. Such systems often employ algorithms which digitalize a worksite into discrete bins or grids to facilitate any necessary computations.
While such algorithms greatly assist in the planning process, there is still room for improvement. For instance, due to the discrete nature of the calculations, precision can be somewhat compromised. One solution for improving precision is to increase the resolution or the number of bins or grids per area of a worksite. By reducing the area or size per bin or grid, a cut location can be more precisely and accurately determined. However, increasing the resolution also significantly increases the number of calculations required per cut location. The increase in computational load would either burden existing control systems, or demand substantial costs for implementing hardware suited to support the added computations.
In view of the foregoing inefficiencies and disadvantages associated with conventional autonomous machines and control systems therefor, a need exists for control systems capable of providing improved precision without substantially increasing computational load.